Helix 2.5: Zero-Shot 30-Home Generalization

Helix 2.5 introduces zero-shot whole-body generalization across 30 unseen homes for three distinct tasks (tidying, folding, bed-making). This capability is primarily driven by pretraining on the 'Index' dataset, which accounts for a 6x increase in zero-shot success rate compared to models trained from scratch (9% vs. 56%). The post quantifies this by demonstrating a human-to-humanoid robot transfer scaling law, showing predictable improvements in downstream action prediction with increased pretraining data. It also highlights that Helix 2.5 requires 2x less adaptation data than previous models for similar or broader generalized behaviors and exhibits enhanced self-correction capabilities.